Keras Deep Learning

Rapidminer has recently launched the Keras deep learning extension that allows users to easily access, and implementing deep learning components within their model in Rapidminer.

With this extension, Keras offers users a set of operators that allows an easy visual representation of deep learning network structures and layers. The operators offered from Keras calculate through a python based backend library in which the users have the option to leverage the computing power of GPUs and grid environments.

The basic idea behind Keras is to allow users to enable fast experimentation with deep learning. The operators provided in the extension offers a more focused approach to deep learning or Deep Neural Networks (DNN). The basic of machine learning to the majority composes of task-specific algorithms. While this can be relatively simple and clear cut, these algorithms have its limitations. Deep learning on the other hand is an approach based on feature learning that is a system, which automatically detects representation, and classify from the given data. From this, the classification obtained will be inputted into a neural net, which is loosely based on biological brain functions such as information processing and communication patterns. Deep learning has been in fact utilized in a wide range of fields such as: computer vision, speech recondition, and natural language processing.

The following information showcases several options to obtain Keras extension for Rapidminer.

Warning: Due to issues with package dependencies, it is not currently possible to install graphviz and pydot in a conda environment on Windows, and consequently to visualise the model graph in the results panel.